Cloud-Based Behavioral Monitoring in Smart Homes

• Main Authors: Niccolò Mora, Guido Matrella, Paolo Ciampolini
• Format: Article
• Language: English
• Published: MDPI AG 2018-06-01
• Series: Sensors
• Subjects: active and assisted living (AAL); smart home; behavioral analysis; deep learning; machine learning
• Online Access: http://www.mdpi.com/1424-8220/18/6/1951

Environmental sensors are exploited in smart homes for many purposes. Sensor data inherently carries behavioral information, possibly useful to infer wellness and health-related insights in an indirect fashion. In order to exploit such features, however, powerful analytics are needed to convert raw sensor output into meaningful and accessible knowledge. In this paper, a complete monitoring architecture is presented, including home sensors and cloud-based back-end services. Unsupervised techniques for behavioral data analysis are presented, including: (i) regression and outlier detection models (also used as feature extractors for more complex models); (ii) statistical hypothesis testing frameworks for detecting changes in sensor-detected activities; and (iii) a clustering process, leveraging deep learning techniques, for extracting complex, multivariate patterns from daily sensor data. Such methods are discussed and evaluated on real-life data, collected within several EU-funded projects. Overall, the presented methods may prove very useful to build effective monitoring services, suitable for practical exploitation in caregiving activities, complementing conventional telemedicine techniques.